Tag: python

[GDS] Geospatial Data Science 101: How to Build an Interactive Map in 10 Minutes Using Python

Introduction

Static maps are great, but interactive maps take spatial data visualization to the next level! Instead of just looking at a map, users can zoom, click, filter, and explore the data dynamically.

Python makes it easy to create interactive maps using libraries like Folium, Plotly, and Leaflet.js. Whether you’re mapping store locations, crime hotspots, or population trends, interactive maps provide a better user experience.

In this article, we’ll cover:
✅ Why interactive maps are useful
✅ The best tools for building them
✅ A step-by-step Python tutorial to create an interactive web map

1️⃣ Why Use Interactive Maps?

Unlike static maps, interactive maps allow users to:
✅ Zoom & Pan — Navigate through different locations.
✅ Click on Markers — Get additional information.
✅ Toggle Layers — View different datasets dynamically.
✅ Filter Data — Customize map views based on user preferences.

💡 Example: A real estate website lets users click on house icons to see property details (price, size, location).

2️⃣ Best Python Libraries for Interactive Mapping

🔹 Folium (Best for Web Maps)

  • Built on Leaflet.js, one of the most popular web mapping frameworks.
  • Simple Python interface for adding markers, popups, and heatmaps.
  • Exports maps as HTML files for easy sharing.

🔹 Plotly (Best for Interactive Data Dashboards)

  • Allows hover effects, filters, and 3D visualizations.
  • Works well for business intelligence dashboards.

🔹 Geopandas + Shapely (Best for Vector Data Processing)

  • Handles shapefiles, GeoJSON, and spatial joins before mapping.

💡 Example: A company can use Folium + Geopandas to map customer locations and identify sales trends by region.

3️⃣ Step-by-Step: Create an Interactive Map with Python (Folium)

🔹 Step 1: Install Folium

First, install Folium using pip:

pip install folium

🔹 Step 2: Create a Basic Interactive Map

Open a Python script and add:

import folium

# Create a map centered on New York City
m = folium.Map(location=[40.7128, -74.0060], zoom_start=12)

# Show the map
m

# optional code
m.save("interactive_map.html")
print("Interactive map created successfully!")

✅ Output: A basic map centered on New York City!

🔹 Step 3: Add Markers for Locations

Let’s add some store locations to our interactive map:

# Add store locations with popup labels
folium.Marker([40.730610, -73.935242], popup="Store A").add_to(m)
folium.Marker([40.758896, -73.985130], popup="Store B").add_to(m)
folium.Marker([40.712776, -74.005974], popup="Store C").add_to(m)

# Show the map
m

✅ Output: Clicking on markers displays store details!

🔹 Step 4: Add a Heatmap (Optional)

If you have customer location data, you can visualize density using a heatmap.

First, install the Heatmap plugin:

pip install branca

Then, modify your Python script:

from folium.plugins import HeatMap

# Sample customer locations (latitude, longitude)
customer_data = [
    [40.730610, -73.935242],
    [40.758896, -73.985130],
    [40.712776, -74.005974],
    [40.750500, -73.976250]
]

# Add heatmap layer
HeatMap(customer_data).add_to(m)

# Show the map
m

✅ Output: A heatmap showing customer density in different areas.

4️⃣ How to Deploy & Share Your Interactive Map

Once you’ve created your HTML map, you can:
✅ Embed it in a website (perfect for businesses).
✅ Share the file with team members for data insights.
✅ Host it on GitHub Pages or a personal server.

💡 Example: A tourism website embeds an interactive map of attractions to help visitors explore a city.

5️⃣ Common Mistakes to Avoid in Interactive Mapping

❌ Not choosing the right map projection — Can lead to distorted views.
❌ Adding too much data at once — Can slow down performance.
❌ Forgetting to test across devices — Make sure it works on mobile & desktop.
❌ Ignoring user experience (UX) — Keep maps clean and simple.

💡 Example: A poorly designed map with too many layers can confuse users instead of helping them.

Conclusion: Why You Should Use Interactive Maps

✅ Engage users with clickable locations & filters.
✅ Improve decision-making with heatmaps & data layers.
✅ Easily share maps without GIS software.

🔗 Useful Resources & Links

Originally published on Medium.

[GDS] Geospatial Data Science 101: Automating GIS Workflows with Python: Save Time, Work Smarter

Introduction

GIS professionals often deal with repetitive tasks — importing spatial data, performing geoprocessing, generating reports. Doing these manually wastes time and increases errors.

What if you could automate these tasks with just a few lines of code? That’s where Python for GIS automation comes in.

In this article, we’ll explore:
✅ Why Python is essential for GIS
✅ Key Python libraries for automation
✅ How to automate common GIS tasks in ArcGIS Pro
✅ A sample script to get you started

1️⃣ Why Automate GIS Workflows?

📌 Automation in GIS allows professionals to:

  • ✅ Save Time — Reduce hours of manual work.
  • ✅ Improve Accuracy — Avoid human errors.
  • ✅ Process Big Data — Handle large datasets efficiently.
  • ✅ Run Batch Processes — Apply the same analysis across multiple datasets.

💡 Example: Instead of manually converting 100 CSV files into shapefiles, a Python script can process them in seconds.

2️⃣ Essential Python Libraries for GIS Automation

🔹 ArcPy (For ArcGIS Pro Users)

  • Automates spatial analysis in ArcGIS Pro.
  • Supports geoprocessing, map exports, and data management.

🔹 GeoPandas (For Open-Source Users)

  • Handles vector data like points, lines, polygons.
  • Reads and writes shapefiles, GeoJSON, and more.

🔹 Rasterio (For Raster Data Processing)

  • Works with satellite imagery and elevation models.

🔹 Folium (For Web Mapping)

  • Creates interactive maps using Python.

🔹 Bonus:

  • Shapely — Geometric operations (buffering, intersections).
  • Fiona — Reads/Writes spatial files.

3️⃣ Automating Common GIS Tasks with Python

🔹 1. Convert CSV to Shapefile Automatically

Instead of manually importing a CSV into ArcGIS Pro, automate it with Python:

import arcpy

input_csv = "C:/GIS/Data/movement_data.csv"
output_shapefile = "C:/GIS/Outputs/movement_points.shp"

# Convert CSV to shapefile
arcpy.management.XYTableToPoint(input_csv, output_shapefile, "Longitude", "Latitude")
print("CSV successfully converted to shapefile!")

✅ Saves time by automating data conversion!

🔹 2. Batch Process Multiple Shapefiles

Let’s say you need to buffer multiple shapefiles in a folder — do it in one go!

import arcpy
import os

arcpy.env.workspace = "C:/GIS/Shapefiles"
output_folder = "C:/GIS/Buffered"

for shapefile in arcpy.ListFeatureClasses():
    output_shp = os.path.join(output_folder, f"buffered_{shapefile}")
    arcpy.analysis.Buffer(shapefile, output_shp, "100 Meters")
    print(f"Buffered: {shapefile}")

✅ Processes all shapefiles in a folder at once!

🔹 3. Automate Map Exporting

Need to export multiple maps in PDF format? Here’s how:

import arcpy

aprx = arcpy.mp.ArcGISProject("C:/GIS/Project.aprx")
layout = aprx.listLayouts()[0]  # Select first layout
output_pdf = "C:/GIS/Maps/output_map.pdf"
layout.exportToPDF(output_pdf)
print("Map exported successfully!")

✅ No more manually clicking ‘Export’ for each map!

4️⃣ Automating GIS Analysis with Python

Example: Find High-Density Customer Locations

Using Kernel Density Estimation (KDE) in Python:

import arcpy

input_points = "C:/GIS/Data/customer_locations.shp"
output_raster = "C:/GIS/Analysis/density_map.tif"
# Perform Kernel Density Analysis
arcpy.sa.KernelDensity(input_points, None, output_raster, 100)
print("Density analysis completed!")

✅ Finds hotspots where customer visits are most frequent.

5️⃣ How to Get Started with GIS Automation

🔹 Step 1: Install Python for GIS

  • ArcGIS users: Python comes pre-installed in ArcGIS Pro.
  • Open-source users: Install GeoPandas, Shapely, Rasterio (pip install geopandas shapely rasterio).

🔹 Step 2: Write Small Scripts

  • Start with simple tasks like converting CSV to shapefiles.
  • Move to batch processing multiple files.

🔹 Step 3: Automate Your Entire Workflow

  • Combine multiple scripts into one automated pipeline.

Conclusion: Why You Should Start Automating GIS Today

GIS automation isn’t just about saving time — it’s about working smarter.

✅ Automate data processing
✅ Automate spatial analysis
✅ Automate map creation & exports

🔗 Useful Resources & Links

Originally published on Medium.